Probabilistic
inference in high-dimensional probabilistic models (i.e., with many
variables) is one of the central problems of statistical machine
learning and stochastic decision making. To date, only a handful of
distinct methods have been developed, most notably (MCMC) sampling,
decomposition, and variational methods. In this talk, I will introduce a
new approach where random projections are used to simplify a
high-dimensional model while preserving some of its key properties.
These random projections can be combined with traditional variational
inference methods (information projections) and combinatorial
optimization tools. These novel randomized approaches provide provable
guarantees on the accuracy, and outperform traditional methods in a
range of domains.